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Title: LSTrAP-Cloud : a user-friendly cloud computing pipeline to infer coexpression networks
Authors: Tan, Qiao Wen
Goh, William
Mutwil, Marek
Keywords: Science::Biological sciences
Issue Date: 2020
Source: Tan, Q. W., Goh, W., & Mutwil, M. (2020). LSTrAP-Cloud : a user-friendly cloud computing pipeline to infer coexpression networks. Genes, 11(4), 428-. doi:10.3390/genes11040428
Journal: Genes
Abstract: As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline.
ISSN: 2073-4425
DOI: 10.3390/genes11040428
Schools: School of Biological Sciences 
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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